The knowledge gap hypothesis suggests that the diffusion of information tends to increase rather than reduce social inequalities. Subsequent research on the digital divide has extended this perspective by focusing on unequal access to and use of digital technologies. The emergence of generative artificial intelligence raises the question of whether these frameworks remain sufficient to describe current forms of informational inequality. While access to AI systems is increasingly widespread, differences may arise in how users engage with AI-generated content. This paper proposes a theoretical extension of the knowledge gap perspective by arguing that generative AI shifts the focus from access and usage to the critical evaluation of information. It is assumed that individuals with higher levels of education are more likely to question and contextualize AI-generated outputs, whereas individuals with lower levels of education may rely more directly on them. The contribution is conceptual and does not present empirical findings. It aims to provide a framework for future research on the relationship between education, AI use, and knowledge inequality.

翻译：知识鸿沟假说认为，信息的传播往往会加剧而非减少社会不平等。随后关于数字鸿沟的研究通过关注对数字技术的获取和使用不平等，扩展了这一观点。生成式人工智能的涌现引发了疑问：这些框架是否仍足以描述当前形式的信息不平等？尽管对人工智能系统的访问日益广泛，但用户如何参与人工智能生成内容方面可能存在差异。本文提出对知识鸿沟视角进行理论扩展，认为生成式人工智能将焦点从获取和使用转向对信息的批判性评估。假设受教育程度较高的个体更有可能质疑并情境化人工智能生成的输出，而受教育程度较低的个体则可能更直接地依赖这些输出。本文为概念性贡献，不呈现实证发现。其旨在为未来研究教育、人工智能使用与知识不平等之间的关系提供一个框架。